MFFCG – Multi feature fusion for hyperspectral image classification using graph attention network

Classification methods that are based on hyperspectral images (HSIs) are playing an increasingly significant role in the processes of target detection, environmental management, and mineral mapping as a result of the fast development of hyperspectral remote sensing technology. Improving classificati...

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Autores:
Aslam Bhatti, Uzair
Huang, Mengxing
NEIRA MOLINA, HAROLD ROBERTO
Marjan, Shah
Baryalai, Mehmood
Tang, Hao
Wu, Guilu
Ullah Bazai, Sibghat
Tipo de recurso:
Article of investigation
Fecha de publicación:
2023
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/10346
Acceso en línea:
https://hdl.handle.net/11323/10346
https://repositorio.cuc.edu.co/
Palabra clave:
Feature fusion
3D-CNN
Graph Attention Network
HSI
Rights
openAccess
License
Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
id RCUC2_8c438948a3021dbc67897b7324029e1f
oai_identifier_str oai:repositorio.cuc.edu.co:11323/10346
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repository_id_str
dc.title.eng.fl_str_mv MFFCG – Multi feature fusion for hyperspectral image classification using graph attention network
title MFFCG – Multi feature fusion for hyperspectral image classification using graph attention network
spellingShingle MFFCG – Multi feature fusion for hyperspectral image classification using graph attention network
Feature fusion
3D-CNN
Graph Attention Network
HSI
title_short MFFCG – Multi feature fusion for hyperspectral image classification using graph attention network
title_full MFFCG – Multi feature fusion for hyperspectral image classification using graph attention network
title_fullStr MFFCG – Multi feature fusion for hyperspectral image classification using graph attention network
title_full_unstemmed MFFCG – Multi feature fusion for hyperspectral image classification using graph attention network
title_sort MFFCG – Multi feature fusion for hyperspectral image classification using graph attention network
dc.creator.fl_str_mv Aslam Bhatti, Uzair
Huang, Mengxing
NEIRA MOLINA, HAROLD ROBERTO
Marjan, Shah
Baryalai, Mehmood
Tang, Hao
Wu, Guilu
Ullah Bazai, Sibghat
dc.contributor.author.none.fl_str_mv Aslam Bhatti, Uzair
Huang, Mengxing
NEIRA MOLINA, HAROLD ROBERTO
Marjan, Shah
Baryalai, Mehmood
Tang, Hao
Wu, Guilu
Ullah Bazai, Sibghat
dc.subject.proposal.eng.fl_str_mv Feature fusion
3D-CNN
Graph Attention Network
HSI
topic Feature fusion
3D-CNN
Graph Attention Network
HSI
description Classification methods that are based on hyperspectral images (HSIs) are playing an increasingly significant role in the processes of target detection, environmental management, and mineral mapping as a result of the fast development of hyperspectral remote sensing technology. Improving classification performance is still a significant problem, however, as a result of the high dimensionality and redundancy of hyperspectral image sets (HSIs), as well as the presence of class imbalance in hyperspectral datasets. In the past few years, convolutional neural networks (CNNs) and graph convolutional networks (GCNs) have achieved good results in HSI classification, but CNNs struggle to achieve good accuracy in low samples, while GCNs have a huge computational cost. To resolve these issues, this paper proposes a Multi-Feature Fusion of 3D-CNN and Graph Attention Network MFFCG. The algorithm consists of two elements: the 3D-CNN, which produces good classification for 3D HSI cube data, and GAT-based encoder and decoder modules that help in improving the classification accuracy of the 3D-CNN. Finally, the multiple features are merged with the help of two neural network models. We further develop two optimized GAT models named GAT1 and GAT2, which are used with different layers of 3D-CNN. Algorithms after merging with 3D-CNN are named MFFCG-1 and MFFCG-2, which produce better classification results then other developed methods. Experiments on three public HSI datasets show that the proposed methods perform better than other state-of-the-art methods using the limited training samples and in low classification time.
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2023-07-27T14:15:18Z
dc.date.available.none.fl_str_mv 2023-07-27T14:15:18Z
2025-11-01
dc.date.issued.none.fl_str_mv 2023-11-01
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.citation.spa.fl_str_mv Uzair Aslam Bhatti, Mengxing Huang, Harold Neira-Molina, Shah Marjan, Mehmood Baryalai, Hao Tang, Guilu Wu, Sibghat Ullah Bazai, MFFCG – Multi feature fusion for hyperspectral image classification using graph attention network, Expert Systems with Applications, Volume 229, Part A, 2023, 120496, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2023.120496.
dc.identifier.issn.spa.fl_str_mv 0957-4174
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/11323/10346
dc.identifier.doi.none.fl_str_mv 10.1016/j.eswa.2023.120496
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv Uzair Aslam Bhatti, Mengxing Huang, Harold Neira-Molina, Shah Marjan, Mehmood Baryalai, Hao Tang, Guilu Wu, Sibghat Ullah Bazai, MFFCG – Multi feature fusion for hyperspectral image classification using graph attention network, Expert Systems with Applications, Volume 229, Part A, 2023, 120496, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2023.120496.
0957-4174
10.1016/j.eswa.2023.120496
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/10346
https://repositorio.cuc.edu.co/
dc.language.iso.spa.fl_str_mv eng
language eng
dc.relation.ispartofjournal.spa.fl_str_mv Expert Systems with Applications
dc.relation.references.spa.fl_str_mv D. Chen et al. Health indicator construction by quadratic function-based deep convolutional auto-encoder and its application into bearing RUL prediction ISA Transactions (2021)
M.E. Paoletti et al. Deep learning classifiers for hyperspectral imaging: A review ISPRS Journal of Photogrammetry and Remote Sensing (2019)
B. Zhang et al. Three-dimensional convolutional neural network model for tree species classification using airborne hyperspectral images Remote Sensing of Environment (2020)
M. Ahmad et al. Hyperspectral image classification—Traditional to deep models: A survey for future prospects IEEE Journal Of Selected Topics In Applied Earth Observations And Remote Sensing (2021)
A. Ari Multipath feature fusion for hyperspectral image classification based on hybrid 3D/2D CNN and squeeze-excitation network Earth Science Informatics (2023)
U.A. Bhatti et al. Recommendation system using feature extraction and pattern recognition in clinical care systems Enterprise Information Systems (2019)
U.A. Bhatti et al. Deep learning with graph convolutional networks: An overview and latest applications in computational intelligence International Journal of Intelligent Systems (2023)
U.A. Bhatti et al. Local similarity-based spatial-spectral fusion hyperspectral image classification with deep CNN and gabor filtering IEEE Transactions on Geoscience and Remote Sensing (2021)
J.M. Bioucas-Dias et al. Hyperspectral remote sensing data analysis and future challenges IEEE Geoscience and Remote Sensing Magazine (2013)
C. Bo et al. Hyperspectral image classification via JCR and SVM models with decision fusion IEEE Geoscience and Remote Sensing Letters (2015)
F. Cao et al. Sparse representation-based augmented multinomial logistic extreme learning machine with weighted composite features for spectral–spatial classification of hyperspectral images IEEE Transactions on Geoscience and Remote Sensing (2018)
Y. Dong et al. Weighted feature fusion of convolutional neural network and graph attention network for hyperspectral image classification IEEE Transactions on Image Processing (2022)
L. Fang et al. Classification of hyperspectral images by exploiting spectral–spatial information of superpixel via multiple kernels IEEE Transactions on Geoscience and Remote Sensing (2015)
H. Firat et al. Classification of hyperspectral remote sensing images using different dimension reduction methods with 3D/2D CNN Remote Sensing Applications: Society and Environment (2022)
H. Fırat et al. Hybrid 3D/2D complete inception module and convolutional neural network for hyperspectral remote sensing image classification Neural Processing Letters (2022)
H. Firat et al. 3D residual spatial–spectral convolution network for hyperspectral remote sensing image classification Neural Computing and Applications (2023)
D. Hong et al. Graph convolutional networks for hyperspectral image classification IEEE Transactions on Geoscience and Remote Sensing (2020)
D. Hong et al. SpectralFormer: Rethinking hyperspectral image classification with transformers IEEE Transactions on Geoscience and Remote Sensing (2021)
L. Huang et al. Dual-path siamese CNN for hyperspectral image classification with limited training samples IEEE Geoscience and Remote Sensing Letters (2020)
dc.relation.citationvolume.spa.fl_str_mv 229
dc.rights.eng.fl_str_mv © 2023 Elsevier Ltd. All rights reserved.
dc.rights.license.spa.fl_str_mv Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
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© 2023 Elsevier Ltd. All rights reserved.
https://creativecommons.org/licenses/by-nc-nd/4.0/
http://purl.org/coar/access_right/c_abf2
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dc.publisher.spa.fl_str_mv Elsevier Ltd.
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spelling Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)© 2023 Elsevier Ltd. All rights reserved.https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Aslam Bhatti, UzairHuang, MengxingNEIRA MOLINA, HAROLD ROBERTOMarjan, ShahBaryalai, MehmoodTang, HaoWu, GuiluUllah Bazai, Sibghat2023-07-27T14:15:18Z2025-11-012023-07-27T14:15:18Z2023-11-01Uzair Aslam Bhatti, Mengxing Huang, Harold Neira-Molina, Shah Marjan, Mehmood Baryalai, Hao Tang, Guilu Wu, Sibghat Ullah Bazai, MFFCG – Multi feature fusion for hyperspectral image classification using graph attention network, Expert Systems with Applications, Volume 229, Part A, 2023, 120496, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2023.120496.0957-4174https://hdl.handle.net/11323/1034610.1016/j.eswa.2023.120496Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Classification methods that are based on hyperspectral images (HSIs) are playing an increasingly significant role in the processes of target detection, environmental management, and mineral mapping as a result of the fast development of hyperspectral remote sensing technology. Improving classification performance is still a significant problem, however, as a result of the high dimensionality and redundancy of hyperspectral image sets (HSIs), as well as the presence of class imbalance in hyperspectral datasets. In the past few years, convolutional neural networks (CNNs) and graph convolutional networks (GCNs) have achieved good results in HSI classification, but CNNs struggle to achieve good accuracy in low samples, while GCNs have a huge computational cost. To resolve these issues, this paper proposes a Multi-Feature Fusion of 3D-CNN and Graph Attention Network MFFCG. The algorithm consists of two elements: the 3D-CNN, which produces good classification for 3D HSI cube data, and GAT-based encoder and decoder modules that help in improving the classification accuracy of the 3D-CNN. Finally, the multiple features are merged with the help of two neural network models. We further develop two optimized GAT models named GAT1 and GAT2, which are used with different layers of 3D-CNN. Algorithms after merging with 3D-CNN are named MFFCG-1 and MFFCG-2, which produce better classification results then other developed methods. Experiments on three public HSI datasets show that the proposed methods perform better than other state-of-the-art methods using the limited training samples and in low classification time.76 páginasapplication/pdfengElsevier Ltd.United Kingdomhttps://www.sciencedirect.com/science/article/abs/pii/S0957417423009983MFFCG – Multi feature fusion for hyperspectral image classification using graph attention networkArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/drafthttp://purl.org/coar/version/c_b1a7d7d4d402bcceExpert Systems with ApplicationsD. Chen et al. Health indicator construction by quadratic function-based deep convolutional auto-encoder and its application into bearing RUL prediction ISA Transactions (2021)M.E. Paoletti et al. Deep learning classifiers for hyperspectral imaging: A review ISPRS Journal of Photogrammetry and Remote Sensing (2019)B. Zhang et al. Three-dimensional convolutional neural network model for tree species classification using airborne hyperspectral images Remote Sensing of Environment (2020)M. Ahmad et al. Hyperspectral image classification—Traditional to deep models: A survey for future prospects IEEE Journal Of Selected Topics In Applied Earth Observations And Remote Sensing (2021)A. Ari Multipath feature fusion for hyperspectral image classification based on hybrid 3D/2D CNN and squeeze-excitation network Earth Science Informatics (2023)U.A. Bhatti et al. Recommendation system using feature extraction and pattern recognition in clinical care systems Enterprise Information Systems (2019)U.A. Bhatti et al. Deep learning with graph convolutional networks: An overview and latest applications in computational intelligence International Journal of Intelligent Systems (2023)U.A. Bhatti et al. Local similarity-based spatial-spectral fusion hyperspectral image classification with deep CNN and gabor filtering IEEE Transactions on Geoscience and Remote Sensing (2021)J.M. Bioucas-Dias et al. Hyperspectral remote sensing data analysis and future challenges IEEE Geoscience and Remote Sensing Magazine (2013)C. Bo et al. Hyperspectral image classification via JCR and SVM models with decision fusion IEEE Geoscience and Remote Sensing Letters (2015)F. Cao et al. Sparse representation-based augmented multinomial logistic extreme learning machine with weighted composite features for spectral–spatial classification of hyperspectral images IEEE Transactions on Geoscience and Remote Sensing (2018)Y. Dong et al. Weighted feature fusion of convolutional neural network and graph attention network for hyperspectral image classification IEEE Transactions on Image Processing (2022)L. Fang et al. Classification of hyperspectral images by exploiting spectral–spatial information of superpixel via multiple kernels IEEE Transactions on Geoscience and Remote Sensing (2015)H. Firat et al. Classification of hyperspectral remote sensing images using different dimension reduction methods with 3D/2D CNN Remote Sensing Applications: Society and Environment (2022)H. Fırat et al. Hybrid 3D/2D complete inception module and convolutional neural network for hyperspectral remote sensing image classification Neural Processing Letters (2022)H. Firat et al. 3D residual spatial–spectral convolution network for hyperspectral remote sensing image classification Neural Computing and Applications (2023)D. Hong et al. Graph convolutional networks for hyperspectral image classification IEEE Transactions on Geoscience and Remote Sensing (2020)D. Hong et al. SpectralFormer: Rethinking hyperspectral image classification with transformers IEEE Transactions on Geoscience and Remote Sensing (2021)L. Huang et al. Dual-path siamese CNN for hyperspectral image classification with limited training samples IEEE Geoscience and Remote Sensing Letters (2020)229Feature fusion3D-CNNGraph Attention NetworkHSIPublicationORIGINALMFFCG – Multi feature fusion for hyperspectral image classification using graph.pdfMFFCG – Multi feature fusion for hyperspectral image classification using graph.pdfArtículoapplication/pdf5236369https://repositorio.cuc.edu.co/bitstreams/99fa3f1e-cf9e-4f0d-965a-c07ba39e8db4/downloadd1ee2524342488023328fd4223731687MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://repositorio.cuc.edu.co/bitstreams/08d3ec2d-f208-436a-b9d7-9525a487376d/download2f9959eaf5b71fae44bbf9ec84150c7aMD52TEXTMFFCG – Multi feature fusion for hyperspectral image classification using graph.pdf.txtMFFCG – Multi feature fusion for hyperspectral image classification using graph.pdf.txtExtracted texttext/plain145765https://repositorio.cuc.edu.co/bitstreams/b5f54141-152d-4c42-9eaa-71671cf8164d/download6787d04f95647f3f2737930cb46e3b3aMD53THUMBNAILMFFCG – Multi feature fusion for hyperspectral image classification using graph.pdf.jpgMFFCG – Multi feature fusion for hyperspectral image classification using graph.pdf.jpgGenerated Thumbnailimage/jpeg10833https://repositorio.cuc.edu.co/bitstreams/9288f466-8e43-4cbe-a1b5-52a30ec15e72/download650213eebc0b52e74386a84d20446afeMD5411323/10346oai:repositorio.cuc.edu.co:11323/103462024-09-17 12:50:27.841https://creativecommons.org/licenses/by-nc-nd/4.0/© 2023 Elsevier Ltd. All rights reserved.open.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa 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ada en las Obras Colectivas.

b.	Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.

c.	Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.
Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).

4. Restricciones.
La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:

a.	Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).

b.	Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.

c.	Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.

d.	Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:

i.	Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.

ii.	Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.

e.	Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.

5. Representaciones, Garantías y Limitaciones de Responsabilidad.
A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

6. Limitación de responsabilidad.
A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

7. Término.

a.	Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.

b.	Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.

8. Varios.

a.	Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.

b.	Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.

c.	Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.

d.	Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.
